Web Page Classification Model Using TCP/IP Header Features
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Solution Overview
Problem
Current methods for identifying and classifying web pages using anonymized TCP/IP headers are limited by resource constraints and do not scale well, particularly when dealing with obfuscated traffic, rendering deep packet inspection infeasible for encrypted or compressed data.
Innovation Solution
A web page classification model is generated by processing TCP/IP headers to extract features that remain stable across time and browser platforms, allowing for accurate discrimination between web page labels, enabling classification of unlabeled web pages without examining packet payloads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection is used to examine packet payloads for web page identification, then identification accuracy is improved, but the method becomes infeasible for obfuscated traffic (encrypted or compressed)
Solution Approach 1:
The patent extracts identification features from TCP/IP headers rather than examining packet payloads. This extraction approach allows classification to work on anonymized headers only, making it feasible for obfuscated traffic where payload inspection is impossible.
Solution Approach 2:
The patent uses TCP/IP header features as an intermediary to infer web page content type. Instead of directly examining the payload content, the system uses header characteristics (such as packet size, timing, and protocol patterns) as indirect indicators of the underlying content type.
2Adaptability or versatility
If TCP/IP headers are processed to extract features for web page classification, then adaptability to obfuscated traffic is improved, but resource constraints limit scalability
Solution Approach 1:
The patent develops a universal classification model that handles multiple content types (video, mobile-optimized, generic web pages) using the same TCP/IP header feature extraction process. This multi-functional approach improves scalability by avoiding the need for separate processing pipelines for different traffic types.
Solution Approach 2:
The patent transforms raw TCP/IP header data into standardized feature parameters (packet size distributions, timing intervals, protocol patterns) that can be processed efficiently at scale. By changing the representation from raw packets to extracted parameters, the system achieves both adaptability and scalability.
3Reliability
If current anonymized TCP/IP header methods are used for protocol identification, then privacy protection is maintained, but the information obtained is not very informative
Solution Approach 1:
The patent adds informational depth by analyzing multiple dimensions of TCP/IP header data simultaneously (packet size, timing, frequency, protocol patterns) rather than relying on single-field header information. This multi-dimensional analysis extracts rich content type information while maintaining privacy through anonymization.
Solution Approach 2:
The patent performs preliminary feature extraction and pattern recognition on TCP/IP headers before final classification. By pre-processing the header data to identify characteristic patterns associated with different content types, the system recovers information that would otherwise be lost in anonymized headers.
Data Source
AI summary
Methods, systems, and computer readable media for generating and using a web page classification model are disclosed. The method may include identifying a plurality of web pages for generating a web page classification model, assigning a label to each of the plurality of web pages, accessing Transmission Control Protocol/Internet Protocol (TCP/IP) traffic traces associated with downloading content from each of the plurality of web pages, processing TCP/IP headers from the TCP/IP traffic traces to identify and extract features that discriminate between the labels, that are uncorrelated and whose discriminatory accuracy remains stable across time and/or browser platform. The method may further include generating a web page classification model by training a trainer to learn a combination of the features that accurately discriminates between the labels. The model is usable to classify unlabeled web pages by applying the model to TCP/IP traffic traces used to access the unlabeled web pages.